1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
28/1/2023 Netflix 8320 Tami NA
26/1/2023 Comida 10370 Andrés NA
29/1/2023 Comida 59172 Tami NA
31/1/2023 Diosi 23990 Andrés Arena diosi
1/2/2023 Comida 6500 Andrés NA
2/2/2023 Comida 4793 Tami NA
2/2/2023 Regalo Matri Chepa 80000 Tami NA
15/2/2023 Comida 68612 Tami NA
15/2/2023 Comida 6800 Andrés NA
19/2/2023 Comida 48617 Tami NA
20/2/2023 VTR 21990 Andrés Entel hogar
22/2/2023 Diosi 3600 Andrés Cobertor arena y juguete
22/2/2023 Transporte 6700 Andrés NA
22/2/2023 Comida 11220 Andrés Fika
21/2/2023 Comida 11170 Tami NA
22/2/2023 Comida 5969 Andrés NA
22/2/2023 Uber 4407 Tami NA
22/2/2023 Uber 6414 Tami NA
22/2/2023 Comida 52690 Tami Restaurant Valpo
22/2/2023 Uber 5215 Tami NA
24/2/2023 Uber 8458 Tami NA
24/2/2023 Comida 7300 Tami Helados Reñaca
25/2/2023 Uber 2889 Tami NA
26/2/2023 Uber 6876 Tami NA
26/2/2023 Enceres 7500 Andrés Merval
26/2/2023 Comida 68970 Andrés Il papparazzo
26/2/2023 Electricidad 40440 Andrés Enel
26/2/2023 Comida 18480 Tami Café Turri Valpo
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 5.5222e+08   2    5.8994 0.0029 ** 
## lag_depvar    8.0072e+10   1 1710.8431 <2e-16 ***
## Residuals     2.5741e+10 550                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838  1040.228 13417.45 0.0171588
## 2-0 27522.895 21854.962 33190.83 0.0000000
## 2-1 20294.057 16902.451 23685.66 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
## 31   25617.86             0   28331.57
## 32   27223.29             0   25617.86
## 33   31622.57             0   27223.29
## 34   32021.43             0   31622.57
## 35   33634.57             0   32021.43
## 36   30784.86             0   33634.57
## 37   34770.57             0   30784.86
## 38   38443.00             1   34770.57
## 39   35073.00             1   38443.00
## 40   31422.29             1   35073.00
## 41   30103.29             1   31422.29
## 42   19319.29             1   30103.29
## 43   27926.29             1   19319.29
## 44   30715.43             1   27926.29
## 45   31962.29             1   30715.43
## 46   39790.14             1   31962.29
## 47   39211.57             1   39790.14
## 48   44548.57             1   39211.57
## 49   49398.00             1   44548.57
## 50   41039.00             1   49398.00
## 51   34821.29             1   41039.00
## 52   29123.57             1   34821.29
## 53   21275.71             1   29123.57
## 54   28476.14             1   21275.71
## 55   24561.86             1   28476.14
## 56   20323.57             1   24561.86
## 57   25370.00             1   20323.57
## 58   26811.86             1   25370.00
## 59   27151.86             1   26811.86
## 60   27623.29             1   27151.86
## 61   22896.57             1   27623.29
## 62   41889.29             1   22896.57
## 63   44000.14             1   41889.29
## 64   38558.00             1   44000.14
## 65   43373.86             1   38558.00
## 66   49001.00             1   43373.86
## 67   61213.29             1   49001.00
## 68   58939.57             1   61213.29
## 69   42046.86             1   58939.57
## 70   39191.71             1   42046.86
## 71   42646.43             1   39191.71
## 72   36121.57             1   42646.43
## 73   30915.57             1   36121.57
## 74   20273.43             1   30915.57
## 75   23938.29             1   20273.43
## 76   19274.29             1   23938.29
## 77   21662.29             1   19274.29
## 78   15819.00             1   21662.29
## 79   18126.14             1   15819.00
## 80   17240.71             1   18126.14
## 81   16127.71             1   17240.71
## 82   13917.14             1   16127.71
## 83   15379.86             1   13917.14
## 84   19510.14             1   15379.86
## 85   24567.29             1   19510.14
## 86   25700.43             1   24567.29
## 87   25729.00             1   25700.43
## 88   26435.00             1   25729.00
## 89   31157.14             1   26435.00
## 90   29818.43             1   31157.14
## 91   30962.43             1   29818.43
## 92   28746.71             1   30962.43
## 93   27830.71             1   28746.71
## 94   28252.14             1   27830.71
## 95   28717.57             1   28252.14
## 96   21365.43             1   28717.57
## 97   24816.86             1   21365.43
## 98   16838.57             1   24816.86
## 99   15529.14             1   16838.57
## 100  13286.29             1   15529.14
## 101  13629.43             1   13286.29
## 102  14404.86             1   13629.43
## 103  19524.86             1   14404.86
## 104  18475.71             1   19524.86
## 105  22495.00             1   18475.71
## 106  22254.57             1   22495.00
## 107  24173.29             1   22254.57
## 108  27466.43             1   24173.29
## 109  24602.43             1   27466.43
## 110  20531.14             1   24602.43
## 111  20846.43             1   20531.14
## 112  23875.71             1   20846.43
## 113  36312.71             1   23875.71
## 114  34244.00             1   36312.71
## 115  36347.43             1   34244.00
## 116  39779.71             1   36347.43
## 117  42018.71             1   39779.71
## 118  39372.57             1   42018.71
## 119  33444.00             1   39372.57
## 120  29255.86             1   33444.00
## 121  31640.14             1   29255.86
## 122  29671.14             1   31640.14
## 123  31023.71             1   29671.14
## 124  39723.43             1   31023.71
## 125  39314.14             1   39723.43
## 126  38239.86             1   39314.14
## 127  34649.43             1   38239.86
## 128  36688.43             1   34649.43
## 129  42867.57             1   36688.43
## 130  42226.86             1   42867.57
## 131  32155.14             1   42226.86
## 132  33603.00             1   32155.14
## 133  37254.43             1   33603.00
## 134  33145.57             1   37254.43
## 135  31299.43             1   33145.57
## 136  30252.00             1   31299.43
## 137  26310.71             1   30252.00
## 138  27929.86             1   26310.71
## 139  27666.14             1   27929.86
## 140  25017.57             1   27666.14
## 141  27335.00             1   25017.57
## 142  25760.71             1   27335.00
## 143  18436.86             1   25760.71
## 144  21906.00             1   18436.86
## 145  19418.14             1   21906.00
## 146  22826.14             1   19418.14
## 147  23444.29             1   22826.14
## 148  25264.86             1   23444.29
## 149  25473.29             1   25264.86
## 150  27366.86             1   25473.29
## 151  28855.86             1   27366.86
## 152  32326.86             1   28855.86
## 153  27141.43             1   32326.86
## 154  26297.71             1   27141.43
## 155  23499.14             1   26297.71
## 156  30246.29             1   23499.14
## 157  39931.86             1   30246.29
## 158  38020.43             2   39931.86
## 159  35004.00             2   38020.43
## 160  40750.86             2   35004.00
## 161  42363.29             2   40750.86
## 162  46273.57             2   42363.29
## 163  41083.29             2   46273.57
## 164  35711.29             2   41083.29
## 165  41921.71             2   35711.29
## 166  60583.29             2   41921.71
## 167  63115.57             2   60583.29
## 168  61300.14             2   63115.57
## 169  57666.43             2   61300.14
## 170  55834.00             2   57666.43
## 171  58927.71             2   55834.00
## 172  57810.57             2   58927.71
## 173  48987.14             2   57810.57
## 174  52219.29             2   48987.14
## 175  56503.57             2   52219.29
## 176  56545.00             2   56503.57
## 177  64705.57             2   56545.00
## 178  53833.29             2   64705.57
## 179  50114.00             2   53833.29
## 180  39592.43             2   50114.00
## 181  29907.29             2   39592.43
## 182  33923.29             2   29907.29
## 183  45489.00             2   33923.29
## 184  44866.29             2   45489.00
## 185  51680.57             2   44866.29
## 186  58257.00             2   51680.57
## 187  70600.57             2   58257.00
## 188  76648.00             2   70600.57
## 189  69430.14             2   76648.00
## 190  69651.57             2   69430.14
## 191  77745.14             2   69651.57
## 192  72795.86             2   77745.14
## 193  67670.71             2   72795.86
## 194  55357.86             2   67670.71
## 195  48524.00             2   55357.86
## 196  50154.43             2   48524.00
## 197  45111.57             2   50154.43
## 198  36147.00             2   45111.57
## 199  43501.57             2   36147.00
## 200  41472.43             2   43501.57
## 201  41058.00             2   41472.43
## 202  41605.57             2   41058.00
## 203  49382.86             2   41605.57
## 204  59558.57             2   49382.86
## 205  59134.57             2   59558.57
## 206  61109.00             2   59134.57
## 207  63004.43             2   61109.00
## 208  67344.29             2   63004.43
## 209  78180.86             2   67344.29
## 210  69117.86             2   78180.86
## 211  55597.57             2   69117.86
## 212  49426.14             2   55597.57
## 213  39119.43             2   49426.14
## 214  35636.86             2   39119.43
## 215  39201.14             2   35636.86
## 216  27777.00             2   39201.14
## 217  47207.00             2   27777.00
## 218  55587.29             2   47207.00
## 219  56619.71             2   55587.29
## 220  82679.86             2   56619.71
## 221  91259.57             2   82679.86
## 222  93552.71             2   91259.57
## 223 102242.71             2   93552.71
## 224  91884.00             2  102242.71
## 225  85013.86             2   91884.00
## 226  84535.29             2   85013.86
## 227  80700.43             2   84535.29
## 228  79740.57             2   80700.43
## 229  85163.14             2   79740.57
## 230  86724.86             2   85163.14
## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  48415.00             2   38864.43
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   398 49757.16 15418.062
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  48415.00
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1998.89206   4031.80429   -529.53230   2445.47461  -2952.74524    524.88382 
##            8            9           10           11           12           13 
##  -5646.82399  -1197.88658  -3977.26337   -439.70579  -4958.33988  -1641.08302 
##           14           15           16           17           18           19 
##   -931.20500    348.70653  -3264.82967   -406.54489  -2154.58522   6577.16970 
##           20           21           22           23           24           25 
##  -1528.03233  -1210.81014   1471.00766  -1183.68308    234.87370   1697.84614 
##           26           27           28           29           30           31 
##  -7091.80996    933.67198   8185.52451    442.76524     10.89189  -2376.92898 
##           32           33           34           35           36           37 
##   1590.44971   4592.40991   1162.23706   2428.22448  -1825.52964   4640.50581 
##           38           39           40           41           42           43 
##   4322.97205  -2243.41938  -2960.96862  -1102.47677 -10738.45164   7254.67650 
##           44           45           46           47           48           49 
##   2552.49843   1371.75429   8114.37766    722.63207   6563.20640   6767.44154 
##           50           51           52           53           54           55 
##  -5812.38171  -4754.62816  -5040.59699  -7929.30387   6101.70638  -4079.65512 
##           56           57           58           59           60           61 
##  -4911.04290   3824.28522    873.85516    -41.10180    134.39914  -5002.63497 
##           62           63           64           65           66           67 
##  18104.09508   3684.16123  -3595.21973   5957.34459   7392.88394  14707.44303 
##           68           69           70           71           72           73 
##   1804.45587 -13109.27347  -1261.41364   4678.34671  -4853.40871  -4380.33367 
##           74           75           76           77           78           79 
## -10491.30227   2436.21405  -5417.58740   1029.84340  -6891.89874    501.09617 
##           80           81           82           83           84           85 
##  -2392.41275  -2734.75752  -3976.60115   -589.85990   2267.31530   3729.55914 
##           86           87           88           89           90           91 
##    461.08934   -496.59888    184.53326   4292.19109  -1156.56010   1152.62386 
##           92           93           94           95           96           97 
##  -2058.79986  -1046.29669    172.39573    271.02325  -7486.21718   2364.33556 
##           98           99          100          101          102          103 
##  -8617.98863  -2983.31370  -4086.47645  -1791.20595  -1314.44047   3130.64559 
##          104          105          106          107          108          109 
##  -2374.81924   2557.61460  -1181.10132    946.87607   2570.01725  -3160.25323 
##          110          111          112          113          114          115 
##  -4738.78385   -879.95127   1874.91752  11675.30181  -1218.27113   2685.71543 
##          116          117          118          119          120          121 
##   4287.22869   3538.85170  -1056.06165  -4681.49547  -3709.55569   2319.98647 
##          122          123          124          125          126          127 
##  -1724.23712   1342.10344   8864.57285    883.26997    165.21646  -2490.18027 
##          128          129          130          131          132          133 
##   2673.84038   7078.28783   1059.39971  -8454.65264   1759.37670   4150.62605 
##          134          135          136          137          138          139 
##  -3136.34464  -1406.23938   -846.83065  -3876.46032   1173.08060   -499.89577 
##          140          141          142          143          144          145 
##  -2918.93677   1703.74317  -1887.57533  -7841.21294   2002.43490  -3504.87879 
##          146          147          148          149          150          151 
##   2068.49082   -279.60563   1002.94947   -373.20251   1338.95782   1179.83986 
##          152          153          154          155          156          157 
##   3354.85091  -4851.65062  -1182.09552  -3246.31880   5936.63174   9749.65611 
##          158          159          160          161          162          163 
##  -3203.58083  -4556.34902   3815.93330    426.43888   2933.30645  -5660.39569 
##          164          165          166          167          168          169 
##  -6514.89877   4371.18637  17627.35364   3917.06695   -102.40077  -2156.01071 
##          170          171          172          173          174          175 
##   -825.74382   3862.87119     53.03557  -7798.05936   3113.77853   4584.88667 
##          176          177          178          179          180          181 
##    897.37841   9021.89143  -8953.15477  -3209.47063 -10493.86733 -11021.29341 
##          182          183          184          185          186          187 
##   1424.41652   9494.70327  -1194.52403   6161.75687   6807.19889  13426.80878 
##          188          189          190          191          192          193 
##   8730.69662  -3750.69349   2752.98027  10653.82572  -1339.90534  -2157.31174 
##          194          195          196          197          198          199 
## -10009.37070  -6126.42007   1452.02953  -5009.91250  -9585.30521   5571.80852 
##          200          201          202          203          204          205 
##  -2858.57227  -1506.88485   -598.60499   6702.08803  10108.64427    827.95304 
##          206          207          208          209          210          211 
##   3171.42078   3348.35532   6038.47810  13097.74470  -5397.14032 -11029.21393 
##          212          213          214          215          216          217 
##  -5432.91863 -10368.17340  -4880.03443   1715.39573 -12811.01367  16562.27906 
##          218          219          220          221          222          223 
##   8031.17106   1769.60523  26931.14767  12828.75511   7654.32585  14348.43075 
##          224          225          226          227          228          229 
##  -3573.84562  -1428.01855   4073.01325    654.69289   3032.60101   9290.60847 
##          230          231          232          233          234          235 
##   6132.65006  -1596.48475  -1532.17503   9709.21487 -11208.30170  -7020.98697 
##          236          237          238          239          240          241 
##  -8300.93068  -9883.47179   3272.81710   1560.54436  -8081.45212  -8791.26638 
##          242          243          244          245          246          247 
##   9276.59529  -7553.93463   2683.39469 -10093.60938  -3867.24223   1606.44245 
##          248          249          250          251          252          253 
##   1197.11459 -12114.15029   3820.23038   2255.48731   4414.55808   2350.52297 
##          254          255          256          257          258          259 
##   -938.69369  11358.58436  21124.79508   3479.95921  -3993.19846   4367.92459 
##          260          261          262          263          264          265 
##  -1434.62360   3986.17322  -4600.38534 -10658.50295  -4519.97843   -322.16992 
##          266          267          268          269          270          271 
##  -4987.52562   8969.77450  -4068.40301   4391.72420  -1895.82341   4636.70592 
##          272          273          274          275          276          277 
##    923.09527   7516.44807  -1187.08911  12242.86891  -4349.10195   1944.29006 
##          278          279          280          281          282          283 
##   -154.83019   8064.05495  -4834.64997  -2521.51875 -11057.67363  -2483.51843 
##          284          285          286          287          288          289 
##  18840.01193   8001.29134   2960.15642   -403.94381   1124.29588   6613.58948 
##          290          291          292          293          294          295 
##   7103.41307 -18546.22577 -10942.75691  -7936.74508   9845.44274   3272.83323 
##          296          297          298          299          300          301 
##   -971.41553  27609.53664  10307.61205   5148.24724   9763.03177   3105.30689 
##          302          303          304          305          306          307 
##   -788.85473   8131.28226 -24056.86463  -3327.49834     31.13688  -6758.37980 
##          308          309          310          311          312          313 
##  -3765.68278   3139.52635  -8976.36205  -3018.64735  -7970.98393   1779.88591 
##          314          315          316          317          318          319 
##  -2927.97179   2273.69428  -3851.05787  27675.57539   -487.82393   3521.69375 
##          320          321          322          323          324          325 
##  11059.75322   5823.70202  32614.80472   5369.06059 -20682.52798   2022.87529 
##          326          327          328          329          330          331 
##   1340.20758  -6236.25653  -1511.64949 -33045.52716   1117.66649  -2053.29383 
##          332          333          334          335          336          337 
##    164.49730  -2901.54247   4356.93221   -157.75168  -6669.71817  -2833.91312 
##          338          339          340          341          342          343 
##  -1906.19697  -7390.97283   4140.28155  -1077.40632  -1442.53711   -697.47246 
##          344          345          346          347          348          349 
##    474.34262    780.63660  -1319.00348  -9148.13962 -12915.91923   2600.18252 
##          350          351          352          353          354          355 
##  -4028.87990  -3363.32045  -5683.43715   2047.58711   1683.44650   3052.53607 
##          356          357          358          359          360          361 
##  -3467.97633   -220.84953    971.44747   7306.48282    568.41921    250.69118 
##          362          363          364          365          366          367 
##   2869.47309  -2465.59396   -594.02396  -8460.01813  -4340.95475  -5924.34871 
##          368          369          370          371          372          373 
##  -4658.07016  -6957.68704   5313.04988    669.63239   7417.98963  -7339.88896 
##          374          375          376          377          378          379 
##  -1966.28558  -3089.89087  -2167.36660 -12156.12583   2206.74140 -10328.80880 
##          380          381          382          383          384          385 
##   6003.42421   9642.79502   3428.82037  -2100.25036   1900.10876   7036.22431 
##          386          387          388          389          390          391 
##  11697.22564  -5525.23621  -5093.74751    107.39535   8825.56622   2071.90218 
##          392          393          394          395          396          397 
##  11473.99441  -9634.66646   3013.87226    949.09607    796.53511   -421.34024 
##          398          399          400          401          402          403 
##   -331.44002 -14255.78212   8766.18806   -932.64428  -1120.11474   7237.91236 
##          404          405          406          407          408          409 
##  -7677.34131  -1035.39375  -2264.61201  -5545.91723  -2577.37390  -3628.74285 
##          410          411          412          413          414          415 
##  -8460.47512   6436.57994   1942.25962  -7072.49791  -7386.93559  14531.77514 
##          416          417          418          419          420          421 
##   4115.31148   4782.55925  -7753.65642  -4459.02590  -2311.33019   3113.62450 
##          422          423          424          425          426          427 
## -13717.45958  -2488.39575  -8791.00796   3328.61494   7292.24424   6884.70702 
##          428          429          430          431          432          433 
##  -3685.41190  -3818.25048  -4417.51937  -1481.79802  -5402.53283  -6313.76922 
##          434          435          436          437          438          439 
##  -5632.51690  -1072.24970   -525.51272  -4652.50746   2906.39288   5161.78030 
##          440          441          442          443          444          445 
##  -4739.22358  -1839.86367   1894.79411  -3520.78929   3152.66200  -6263.75243 
##          446          447          448          449          450          451 
## -11793.93415  -4189.39326   9970.23776  -1710.33272   5076.40985  -5550.13582 
##          452          453          454          455          456          457 
##   -801.70336    705.04066   3346.94141 -11949.73072   3690.96496  -6380.29038 
##          458          459          460          461          462          463 
##   6844.97927   3332.89086   2825.52674  -3529.59942   2408.80031    307.15724 
##          464          465          466          467          468          469 
##   2106.34921   -209.54442   3661.04577  -2330.69071   6113.98600  -6635.02182 
##          470          471          472          473          474          475 
##  -2654.20536  -1892.30011  -4347.96396   3315.53955   8116.92540  -5699.56189 
##          476          477          478          479          480          481 
##   1803.29021  -5859.53174  -2522.83516   2335.47573 -12605.84741  -9428.85543 
##          482          483          484          485          486          487 
##   -873.56715    350.97238   -630.28648  -1009.18392  -9251.57042  11429.33381 
##          488          489          490          491          492          493 
##   6568.74337   7751.78979  -5108.20973   5694.14867   9617.48601   6374.56124 
##          494          495          496          497          498          499 
## -13156.84953 -10249.04048  -3121.79857   -783.87983   -200.20443  -7300.08728 
##          500          501          502          503          504          505 
##    939.63484   4618.47821   5840.33138    991.58978    411.04338  -6909.12128 
##          506          507          508          509          510          511 
##    900.28524  -4717.29917   2164.44981   -963.80145  -7824.83922   -268.37722 
##          512          513          514          515          516          517 
##  -2339.67472   -252.07739   1668.56177  -9158.53275  -7428.58160  24623.76560 
##          518          519          520          521          522          523 
##  10162.40361   6212.96523  -5006.39171   3124.10925  17342.16506  11792.32253 
##          524          525          526          527          528          529 
## -23835.95561  -4749.16368  -3421.98344   4884.76569    -43.56897 -10789.39682 
##          530          531          532          533          534          535 
##   4706.20449  14225.95362  -4661.32275   4687.61610   5867.37173  -1480.15909 
##          536          537          538          539          540          541 
##  -4232.10249  -6765.07132  -1789.64756   8635.89720    441.70349  -7826.76068 
##          542          543          544          545          546          547 
##   2131.92223   -282.10905    684.88401 -10711.29987 -10740.34144   2364.86416 
##          548          549          550          551          552          553 
##   7330.43976   -989.26820   1165.88905  -7391.25831   8895.13586   1237.29842 
##          554          555 
## -11614.40663   8120.05415 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17270.39 20107.20 24345.68 24064.67 26409.46 23751.83 24465.54 19715.03 
##       10       11       12       13       14       15       16       17 
## 19452.55 16804.99 17579.63 14320.94 14371.92 15034.15 16724.54 15050.69 
##       18       19       20       21       22       23       24       25 
## 16081.59 15457.40 22514.03 21601.38 21083.14 22966.25 22294.70 22944.87 
##       26       27       28       29       30       31       32       33 
## 24784.10 18734.61 20454.48 28263.23 28320.68 27994.79 25632.84 27030.16 
##       34       35       36       37       38       39       40       41 
## 30859.19 31206.35 32610.39 30130.07 34120.03 37316.42 34383.25 31205.76 
##       42       43       44       45       46       47       48       49 
## 30057.74 20671.61 28162.93 30590.53 31675.77 38488.94 37985.37 42630.56 
##       50       51       52       53       54       55       56       57 
## 46851.38 39575.91 34164.17 29205.02 22374.44 28641.51 25234.61 21545.71 
##       58       59       60       61       62       63       64       65 
## 25938.00 27192.96 27488.89 27899.21 23785.19 40315.98 42153.22 37416.51 
##       66       67       68       69       70       71       72       73 
## 41608.12 46505.84 57135.12 55156.13 40453.13 37968.08 40974.98 35295.91 
##       74       75       76       77       78       79       80       81 
## 30764.73 21502.07 24691.87 20632.44 22710.90 17625.05 19633.13 18862.47 
##       82       83       84       85       86       87       88       89 
## 17893.74 15969.72 17242.83 20837.73 25239.34 26225.60 26250.47 26864.95 
##       90       91       92       93       94       95       96       97 
## 30974.99 29809.80 30805.51 28877.01 28079.75 28446.55 28851.65 22452.52 
##       98       99      100      101      102      103      104      105 
## 25456.56 18512.46 17372.76 15420.63 15719.30 16394.21 20850.53 19937.39 
##      106      107      108      109      110      111      112      113 
## 23435.67 23226.41 24896.41 27762.68 25269.93 21726.38 22000.80 24637.41 
##      114      115      116      117      118      119      120      121 
## 35462.27 33661.71 35492.49 38479.86 40428.63 38125.50 32965.41 29320.16 
##      122      123      124      125      126      127      128      129 
## 31395.38 29681.61 30858.86 38430.87 38074.64 37139.61 34014.59 35789.28 
##      130      131      132      133      134      135      136      137 
## 41167.46 40609.80 31843.62 33103.80 36281.92 32705.67 31098.83 30187.17 
##      138      139      140      141      142      143      144      145 
## 26756.78 28166.04 27936.51 25631.26 27648.29 26278.07 19903.57 22923.02 
##      146      147      148      149      150      151      152      153 
## 20757.65 23723.89 24261.91 25846.49 26027.90 27676.02 28972.01 31993.08 
##      154      155      156      157      158      159      160      161 
## 27479.81 26745.46 24309.65 30182.20 41224.01 39560.35 36934.92 41936.85 
##      162      163      164      165      166      167      168      169 
## 43340.26 46743.68 42226.18 37550.53 42955.93 59198.50 61402.54 59822.44 
##      170      171      172      173      174      175      176      177 
## 56659.74 55064.84 57757.54 56785.20 49105.51 51918.68 55647.62 55683.68 
##      178      179      180      181      182      183      184      185 
## 62786.44 53323.47 50086.30 40928.58 32498.87 35994.30 46060.81 45518.81 
##      186      187      188      189      190      191      192      193 
## 51449.80 57173.76 67917.30 73180.84 66898.59 67091.32 74135.76 69828.03 
##      194      195      196      197      198      199      200      201 
## 65367.23 54650.42 48702.40 50121.48 45732.31 37929.76 44331.00 42564.88 
##      202      203      204      205      206      207      208      209 
## 42204.18 42680.77 49449.93 58306.62 57937.58 59656.07 61305.81 65083.11 
##      210      211      212      213      214      215      216      217 
## 74515.00 66626.79 54859.06 49487.60 40516.89 37485.75 40588.01 30644.72 
##      218      219      220      221      222      223      224      225 
## 47556.11 54850.11 55748.71 78430.82 85898.39 87894.28 95457.85 86441.88 
##      226      227      228      229      230      231      232      233 
## 80462.27 80045.74 76707.97 75872.53 80592.21 81951.48 76407.32 71637.79 
##      234      235      236      237      238      239      240      241 
## 77270.73 63967.42 56033.07 48013.19 39655.47 43832.03 45976.88 39451.55 
##      242      243      244      245      246      247      248      249 
## 33154.26 43399.08 37667.03 41588.32 33880.53 32591.13 36233.03 39046.58 
##      250      251      252      253      254      255      256      257 
## 29909.63 35825.94 39613.44 44789.19 47497.55 46991.99 57255.20 74688.33 
##      258      259      260      261      262      263      264      265 
## 74504.06 67839.22 69315.62 65550.26 66991.10 60771.65 50085.55 46127.46 
##      266      267      268      269      270      271      272      273 
## 46336.10 42457.08 51228.97 47515.70 51647.25 49770.72 53823.19 54118.12 
##      274      275      276      277      278      279      280      281 
## 60113.52 57756.42 67393.96 61341.00 61550.26 59905.37 65627.22 59380.66 
##      282      283      284      285      286      287      288      289 
## 55957.10 45547.66 43950.27 61119.42 66629.27 67037.23 64464.28 63554.98 
##      290      291      292      293      294      295      296      297 
## 67541.30 71437.23 52503.33 42641.60 36674.56 46958.17 50188.13 49305.32 
##      298      299      300      301      302      303      304      305 
## 73413.10 79336.75 80001.97 84597.55 82802.71 77851.15 81305.29 56295.93 
##      306      307      308      309      310      311      312      313 
## 52570.72 52251.67 46064.54 43284.19 46874.36 39453.79 38180.56 32761.97 
##      314      315      316      317      318      319      320      321 
## 36532.69 35717.02 39534.49 37526.28 63218.40 61067.45 62685.10 70654.01 
##      322      323      324      325      326      327      328      329 
## 73032.62 98421.23 96804.81 72723.27 71525.51 69888.83 61869.94 59002.67 
##      330      331      332      333      334      335      336      337 
## 29060.76 32734.87 33172.79 35484.26 34827.50 40573.47 41645.15 36910.06 
##      338      339      340      341      342      343      344      345 
## 36127.34 36253.54 31589.58 37566.69 38227.68 38485.19 39357.80 41137.22 
##      346      347      348      349      350      351      352      353 
## 42952.57 42705.14 35675.49 26277.67 31602.88 30468.03 30059.58 27684.70 
##      354      355      356      357      358      359      360      361 
## 32346.55 36087.18 40534.55 38730.14 39985.84 42116.52 49484.87 50033.45 
##      362      363      364      365      366      367      368      369 
## 50234.38 52688.59 50181.17 49627.73 42299.67 39506.63 35697.50 33484.26 
##      370      371      372      373      374      375      376      377 
## 29556.38 36817.80 39096.44 46953.32 40946.86 40396.03 38938.65 38473.13 
##      378      379      380      381      382      383      384      385 
## 29373.97 33955.38 27032.29 35221.78 45517.32 49069.82 47349.46 49333.92 
##      386      387      388      389      390      391      392      393 
## 55531.49 64982.52 58218.46 52706.75 52436.43 59789.24 60310.72 68947.95 
##      394      395      396      397      398      399      400      401 
## 58093.13 59654.33 59216.04 58701.77 57194.15 55960.21 42766.81 51321.36 
##      402      403      404      405      406      407      408      409 
## 50325.40 49295.37 55673.48 48242.97 47556.61 45889.35 41582.23 40417.17 
##      410      411      412      413      414      415      416      417 
## 38488.05 32603.56 40447.88 43363.64 38055.22 33161.22 47979.12 51810.01 
##      418      419      420      421      422      423      424      425 
## 55725.08 48221.45 44558.04 43238.80 46812.32 35273.25 35003.44 29282.96 
##      426      427      428      429      430      431      432      433 
## 34852.61 43150.15 50017.41 46794.54 43873.81 40810.08 40698.68 37189.20 
##      434      435      436      437      438      439      440      441 
## 33341.52 30585.54 32155.94 33998.65 32010.46 36859.08 43042.22 39806.29 
##      442      443      444      445      446      447      448      449 
## 39513.35 42508.93 40402.62 44377.75 39641.79 30706.39 29548.05 40864.05 
##      450      451      452      453      454      455      456      457 
## 40546.73 46177.56 41829.42 42177.82 43792.49 47497.30 37408.04 42239.86 
##      458      459      460      461      462      463      464      465 
## 37679.59 45221.39 48728.76 51339.89 48081.20 50413.56 50614.37 52355.12 
##      466      467      468      469      470      471      472      473 
## 51854.53 54787.69 52125.59 57158.59 50442.78 48062.30 46653.54 43290.03 
##      474      475      476      477      478      479      480      481 
## 47032.65 54469.13 48916.14 50613.25 45420.84 43805.67 46628.42 36080.71 
##      482      483      484      485      486      487      488      489 
## 29665.42 31528.03 34215.00 35699.61 36662.00 30325.67 42810.83 49447.07 
##      490      491      492      493      494      495      496      497 
## 56252.78 50983.28 55798.94 63405.15 67202.85 53508.61 44120.37 42152.45 
##      498      499      500      501      502      503      504      505 
## 42474.49 43262.80 37769.37 40159.66 45442.10 51103.27 51810.39 51920.55 
##      506      507      508      509      510      511      512      513 
## 45645.14 46980.30 43252.98 45998.52 45665.41 39403.81 40530.82 39708.93 
##      514      515      516      517      518      519      520      521 
## 40810.58 43441.10 36307.01 31603.38 55407.02 63538.32 67178.11 60581.03 
##      522      523      524      525      526      527      528      529 
## 61915.69 75452.39 82403.96 57444.45 52332.98 49039.23 53402.43 52910.54 
##      530      531      532      533      534      535      536      537 
## 43129.51 48103.33 60718.18 55258.81 58644.20 62617.59 59680.82 54729.50 
##      538      539      540      541      542      543      544      545 
## 48215.36 46876.10 54784.58 54535.90 47122.79 49338.39 49165.69 49857.01 
##      546      547      548      549      550      551      552      553 
## 40539.77 32404.99 36731.13 44818.41 44616.11 46315.83 40347.29 49327.70 
##      554      555 
## 50478.84 40294.95 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8396
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##       original     bias    std. error
## t1*    5.89944  0.5708352    3.255247
## t2* 1710.84311 26.4811895  233.301611
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.957402       6.034588   12.53788
## 2    lag_depvar 1381.690716    1723.426504 2141.53344

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Feb 27 00:39:04 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Feb 27 00:39:12 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Feb 27 00:39:19 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Feb 27 00:39:27 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Feb 27 00:39:35 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Feb 27 00:39:43 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Feb 27 00:39:50 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Feb 27 00:39:58 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Feb 27 00:40:06 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Feb 27 00:40:14 2023
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua 0.000 5.410333 5.629750 6.874784
Comida 306.371 310.278417 314.087500 339.450676
Comunicaciones 0.000 0.000000 0.000000 0.000000
Electricidad 0.000 47.072333 38.297667 31.524297
Enceres 0.000 20.086417 17.443792 23.967432
Farmacia 0.000 1.831667 7.913875 9.685784
Gas/Bencina 70.300 44.325000 28.954333 26.278486
Diosi 23.990 31.180667 41.934250 39.888324
donaciones/regalos 0.000 0.000000 7.170083 7.424838
Electrodomésticos/ Mantención casa 0.000 3.944000 30.269500 22.418054
VTR 0.000 25.156667 22.121792 20.548324
Netflix 8.320 7.151583 7.090167 7.498676
Otros 0.000 3.151083 1.575542 1.021973
Total 408.981 499.588167 522.488250 536.581649
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1894, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-03-09 00:04:58 sería de: 36.878 pesos// Percentil 95% más alto proyectado: 40.709,42

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 35700.96 35678.65
Lo.80 35801.88 35789.68
Point.Forecast 36878.16 38619.49
Hi.80 38953.56 43214.60
Hi.95 40099.08 45647.10


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.3355  996.7419
## s.e.  0.1418   34.6664
## 
## sigma^2 = 26892:  log likelihood = -311.94
## AIC=629.87   AICc=630.42   BIC=635.49
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.3262   780.4780   7.1703
## s.e.  0.1432   435.8235  14.3957
## 
## sigma^2 = 27354:  log likelihood = -311.82
## AIC=631.63   AICc=632.56   BIC=639.12
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 725.2132 655.5615 697.3647
Lo.80 843.9093 773.6558 779.0319
Point.Forecast 1068.1315 996.7414 960.2959
Hi.80 1292.3537 1219.8269 1259.7297
Hi.95 1411.0498 1337.9213 1454.3580


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Andrés Tami
1 marzo_2019 68268 175533
2 abril_2019 55031 152640
3 mayo_2019 192219 152985
4 junio_2019 84961 291067
5 julio_2019 205893 241389


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.6      
##  [7] tidytext_0.4.1      DT_0.27             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.1       xts_0.13.0         
## [13] forecast_8.20       wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.0     tm_0.7-11           NLP_0.2-1          
## [19] tsibble_1.1.3       lubridate_1.9.2     forcats_1.0.0      
## [22] dplyr_1.1.0         purrr_1.0.1         tidyr_1.3.0        
## [25] tibble_3.1.8        ggplot2_3.4.1       tidyverse_2.0.0    
## [28] sjPlot_2.8.12       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.7.9      httr_1.4.5         
## [34] readxl_1.4.2        zoo_1.8-11          stringr_1.5.0      
## [37] stringi_1.7.12      data.table_1.14.8   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.8          readr_2.1.4        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     systemfonts_1.0.4  
##   [4] selectr_0.4-2       lazyeval_0.2.2      splines_4.1.2      
##   [7] crosstalk_1.2.0     digest_0.6.31       htmltools_0.5.4    
##  [10] fansi_1.0.4         ggfortify_0.4.15    magrittr_2.0.3     
##  [13] tzdb_0.3.0          modelr_0.1.10       vroom_1.6.1        
##  [16] timechange_0.2.0    anytime_0.3.9       tseries_0.10-53    
##  [19] colorspace_2.1-0    xfun_0.37           crayon_1.5.2       
##  [22] jsonlite_1.8.4      lme4_1.1-31         glue_1.6.2         
##  [25] r2d3_0.2.6          gtable_0.3.1        emmeans_1.8.4-1    
##  [28] sjstats_0.18.2      sjmisc_2.8.9        car_3.1-1          
##  [31] quantmod_0.4.20     abind_1.4-5         mvtnorm_1.1-3      
##  [34] DBI_1.1.3           ggeffects_1.2.0     Rcpp_1.0.10        
##  [37] viridisLite_0.4.1   xtable_1.8-4        performance_0.10.2 
##  [40] bit_4.0.5           datawizard_0.6.5    htmlwidgets_1.6.1  
##  [43] timeSeries_4021.105 gplots_3.1.3        ellipsis_0.3.2     
##  [46] spatial_7.3-14      pkgconfig_2.0.3     farver_2.1.1       
##  [49] nnet_7.3-16         sass_0.4.5          dbplyr_2.3.1       
##  [52] janitor_2.2.0       utf8_1.2.3          tidyselect_1.2.0   
##  [55] labeling_0.4.2      rlang_1.0.6         munsell_0.5.0      
##  [58] cellranger_1.1.0    tools_4.1.2         cachem_1.0.6       
##  [61] cli_3.6.0           generics_0.1.3      sjlabelled_1.2.0   
##  [64] broom_1.0.3         evaluate_0.20       fastmap_1.1.0      
##  [67] yaml_2.3.7          knitr_1.42          bit64_4.0.5        
##  [70] caTools_1.18.2      forge_0.2.0         nlme_3.1-153       
##  [73] slam_0.1-50         xml2_1.3.3          tokenizers_0.3.0   
##  [76] compiler_4.1.2      rstudioapi_0.14     curl_5.0.0         
##  [79] bslib_0.4.2         highr_0.10          fBasics_4021.93    
##  [82] Matrix_1.5-3        its.analysis_1.6.0  nloptr_2.0.3       
##  [85] urca_1.3-3          vctrs_0.5.2         pillar_1.8.1       
##  [88] lifecycle_1.0.3     lmtest_0.9-40       jquerylib_0.1.4    
##  [91] estimability_1.4.1  bitops_1.0-7        insight_0.19.0     
##  [94] R6_2.5.1            KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] codetools_0.2-18    gtools_3.9.4        boot_1.3-28        
## [100] MASS_7.3-54         assertthat_0.2.1    rprojroot_2.0.3    
## [103] withr_2.5.0         fracdiff_1.5-2      bayestestR_0.13.0  
## [106] parallel_4.1.2      hms_1.1.2           quadprog_1.5-8     
## [109] timeDate_4022.108   minqa_1.2.5         snakecase_0.11.0   
## [112] rmarkdown_2.20      carData_3.0-5       TTR_0.24.3         
## [115] base64enc_0.1-3
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))